A Projection Pursuit Forest Algorithm for Supervised Classification

Silva, Natalia da; Cook, Dianne; Lee, Eunkyung

doi:10.1080/10618600.2020.1870480

Cited by 10 publications

(5 citation statements)

References 25 publications

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“… Genetic projection pursuit model The weight of 13 indicators is calculated using the genetic projection pursuit model. For this purpose, high dimensional data are projected onto a lower-dimensional space to construct objective functions and identify the best projection path capable of reflecting the structural feature of high dimensional data 39 , 40 . The configuration of the genetic projection pursuit model requires three procedures that are, data standardization, projection indicator function construction, and projection indicator function optimization.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Jiang

Fan

Zhang

et al. 2022

Sci Rep

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The contradiction between the exploitation of coal resources and the protection of the ecological environment in western China is becoming increasingly prominent. Reasonable ecological environment evaluation is the premise for alleviating this contradiction. First, this paper evaluates the eco-environment of Ibei coalfield by combining the genetic projection pursuit model and geographic information system (GIS) and using remote sensing image data and other statistical data of this area. The powerful spatial analysis function of GIS and the advantages of the genetic projection pursuit model in weight calculation have been fully used to improve the reliability of the evaluation results. Furthermore, spatial autocorrelation is used to analyze the spatial characteristics of ecological environment quality in the mining area and plan the specific governance scope. The geographic detector is used to determine the driving factors of the eco-environment of the mining area. The results show that Ibei Coalfield presents a spatially heterogeneous eco-environment pattern. The high-intensity mining area (previously mined area of Ili No.4 Coal Mine) has the worst ecological environment quality, followed by the coal reserve area of Ili No.4 Coal Mine and the planned survey area of Ili No.5 Coal Mine. The eco-environment quality (EEQ) of the study area is affected by both human and natural factors. Mining intensity and surface subsidence are the main human factors affecting the ecological environment in the study area. The main natural factors affecting the ecological environment in the study area are annual average precipitation, elevation, annual average evaporation, NDVI and land use type. Meanwhile, the interaction effect of any two indicators is greater than that of a single indicator. It is also indicated that the eco-environment of the mining area is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis shows three areas that should be treated strategically that are the management area, close attention area and protective area. Corresponding management measures are put forward for different regions. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.

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Section: Methodsmentioning

confidence: 99%

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Jiang

Fan

Zhang

et al. 2022

Sci Rep

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“…[188] introduced the Projection Pursuits Dynamic Cluster (PPDC) to address issues of HDD and non-linearity. [189] also proposed the Projection Pursuits Random Forest (PPRF) technique to solve problems of classification. Experimental results revealed that PPRF was more efficient than Random Forest (RF) when there was a separation of classes applying linear combination of features or when there is an increase in correlation between features.…”

Section: Project Pursuit (Pp)mentioning

confidence: 99%

Review of Dimension Reduction Methods

Nanga¹,

Bawah²,

Acquaye³

et al. 2021

JDAIP

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Purpose: This study sought to review the characteristics, strengths, weaknesses variants, applications areas and data types applied on the various Dimension Reduction techniques. Methodology: The most commonly used databases employed to search for the papers were ScienceDirect, Scopus, Google Scholar, IEEE Xplore and Mendeley. An integrative review was used for the study where 341 papers were reviewed. Results: The linear techniques considered were Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), Latent Semantic Analysis (LSA), Locality Preserving Projections (LPP), Independent Component Analysis (ICA) and Project Pursuit (PP). The non-linear techniques which were developed to work with applications that have complex non-linear structures considered were Kernel Principal Component Analysis (KPCA), Multi-dimensional Scaling (MDS), Isomap, Locally Linear Embedding (LLE), Self-Organizing Map (SOM), Latent Vector Quantization (LVQ), t-Stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). DR techniques can further be categorized into supervised, unsupervised and more recently semi-supervised learning methods. The supervised versions are the LDA and LVQ. All the other techniques are unsupervised. Supervised variants of PCA, LPP, KPCA and MDS have been developed. Supervised and semi-supervised variants of PP and t-SNE have also been developed and a semi supervised version of the LDA has been developed. Conclusion: The various application areas, strengths, weaknesses and variants of the DR tech

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“…The weight of 13 indicators is calculated using genetic projection pursuit model. For this purpose, high dimensional data are projected onto a lower dimensional space to construct objective functions and identify the best projection path capable of re ecting the structural feature of high dimensional data 27,28 . The basic procedures for projection pursuit modelling are as follows:…”

Section: Indicator Weight Determinationmentioning

confidence: 99%

Evaluation of Eco-environmental Quality of Coal Mining Area Using Multi-source Data – A Case Regarding Ibei Coalfield, Northwestern China

Jiang

Fan

Zhang

et al. 2021

Preprint

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Eco-environmental evaluation is a prerequisite for balancing the relationship between coal resource recovery and eco-environmental protection. This paper divides the eco-environment system in coal mining area into 5 subsystems regarding geomorphology, climate, hydrology, land and vegetation, and human activity. Within the 5 subsystems, 13 indicators capable of reflecting eco-environment levels of coal mine fields are selected, weighed using genetic projection pursuit model, and applied to eco-environmental quality evaluation. Based on this, the spatial feature of the quality is analysed using spatial autocorrelation method, recognising the areas that need managements. Factors driving the eco-environment characteristics of coal mines are identified using geographic detector. The feasibility of the developed evaluation system is verified with Ibei Coalfield as a case. The results show that Ibei Coalfield sees a spatially heterogeneous eco-environment pattern. Geographic detector can quantify the impact of various indicators on ecological environment, and the indicator is of stronger interpretation ability as interacting with others. It is also indicated that mining area eco-environment is nonlinearly correlated to impact indicators. The spatial autocorrelation analysis suggests three areas that should be treated strategically, that are the management area, close attention area and protective area. This paper can provide scientific references for mining area eco-environmental protection, which is significant for the sustainability of coal mine projects.

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A Projection Pursuit Forest Algorithm for Supervised Classification

Cited by 10 publications

References 25 publications

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Evaluation of eco-environmental quality for the coal-mining region using multi-source data

Review of Dimension Reduction Methods

Evaluation of Eco-environmental Quality of Coal Mining Area Using Multi-source Data – A Case Regarding Ibei Coalfield, Northwestern China

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